6 CONTENTS Carl Bronn ECONOMIC SCIENCES: ON THE AUTOBAHN, OR ON THE ROAD TO NOWHERE? Kenneth J. Thomson CHALLENGES FOR RESEARCH IN THE FIELD OF AGRICULTURAL ECONOMICS IN THE UK Wojciech J. Florkowski ORGANIZATION, FINANCING AND THE LATEST TRENDS IN AGRICULTURAL ECONOMICS RESEARCH IN THE USA Peter Bielik, Daniela Hupková SITUATION OF ECONOMIC RESEARCH IN SLOVAKIA Bogdan Klepacki POLISH ECONOMIC SCIENCES DEVELOPMENT AFTER NEW PACKET LAW REGULATION INPLEMENTATION Joanna Paliszkiewicz THE POSSIBILITY AND CONDITIONS OF PUBLISHING IN HIGH-QUALITY JOURNALS Eugeniusz Niedzielski, Miros³aw Gornowicz THE DEVELOPMENT OF ECONOMIC SCIENCES IN WARMIA AND MAZURY Jacek Chotkowski PROBLEMS OF THE METHODOLOGY OF EMPIRICAL ECONOMIC RESEARCH Gra yna Karmowska RESEARCH AND THE MEASUREMENT OF REGIONAL DEVELOPMENT ON THE EXAMPLE OF ZACHODNIOPOMORSKIE PROVINCE Piotr Sulewski FARMER S RISK AVERSION IMPACT ON FARM S RESULTS Tomasz Pawlonka LIQUIDITY AND CASH CONVERSION CYCLE IN SELECTED ENTERPRISES IN THE MEAT INDUSTRY Ryszard Kata GOVERNMENT INTERVENTION IN AGRICULTURAL CREDIT MARKET AND THE PROBLEM OF FARMERS' ACCESS TO BANK CREDIT Jaros³aw W. Przybytniowski INSURANCE SERVICES IN SELECTED EUROPEAN UNION COUNTRIES

7 ROCZNIKI ECONOMIC NAUK SCIENCES: ROLNICZYCH, ON THE SERIA AUTOBAHN... G, T. 98, z. 2, ECONOMIC SCIENCES: ON THE AUTOBAHN, OR ON THE ROAD TO NOWHERE? Carl Bronn School of Economics and Business, The Norwegian University of Life Sciences, As, Norway S³owa kluczowe: modele myœlowe, modelowanie ekonomiczne, badania ekonomiczne Key words: mental models, economic modeling, economic research A b s t r a c t. Model building is a fundamental activity in economic science. Addressing the complexities of representing the "real world" has resulted in significant methodological advances, which in turn play an important role in furthering the field. However, what is not as well appreciated is that there is another type of modeling involved in this activity. While the econometric models are explicitly presented and open for inspection, the other model, called the "mental model", is tacit, personal, and only rarely subjected to scrutiny. The two models are not independent; mental models define the framework within which the "public" econometric models are developed and understanding the nature of this interaction is important for the further development of economic sciences. There have are signals that the traditional approach to economic modeling is insufficient to meet the needs given current developments in understanding human behavior and policy analysis. The resistance to change has many possible causes, in this paper we briefly consider the role that mental models play in this development process and point to some alternative approaches that may be better suited to meet current modeling needs. INTRODUCTION Model building has been a fundamental activity in economic science since the adoption of physics-based mathematical techniques in the 1700 s. Addressing the complexities and challenges of data collection, model specification, verification, and finally application has resulted in significant methodological advances, which in turn play an important role in furthering the field. However, what is not as well appreciated is that there is another type of modeling that is involved in this activity. While the econometric models are explicitly presented and open for inspection, the other model, called the mental model, is tacit, personal, and only rarely subjected to scrutiny. The two models are not independent; mental models define the framework within which the public econometric models are developed. Understanding the nature of this interaction of models is important for the further development of economic sciences. There have already been signals that the traditional approach to economic modeling is insufficient to meet the needs of current developments

8 8 C. BRONN in understanding human behavior and policy analysis. The resistance to change has many possible causes, in this paper we briefly consider the role that mental models play in this development process and point to some alternative approaches that seem better suited to meet the modeling needs of today. The paper is organized as follows. The next section presents a brief consideration of the function of models in general and in econometric analyses, in particular. Following this, we offer some cautionary words that these models may not be as helpful as desired, which leads into a critical perspective on models in social sciences. The role of mental models is then presented and the reality that economics is concerned with is reconsidered in light of the previous challenges. An alternative paradigm for thinking about econometrics is offered in the form of viewing reality as a complex adaptive system. The characteristics of these systems are briefly discussed and we present policy design issues that need to be considered when attempting to develop policies for this type of process. We conclude with some reflections on learning processes and challenges to implementing this perspective. THE FUNCTION OF MODELS Model building is a combination of theory and practice with the intent to produce simplified representations of a complex reality that still captures the essence of the phenomenon of interest. Model builders strive to achieve two objectives with their models: the ability to predict and the ability to explain. The first is associated with practice, the latter with theory. In the case of economic sciences, a case can be made that theory has become the dominant component. The relation to practice follows from the application of theoretical models to a variety of questions posed by the real world As an example of the two perspectives on the role of modeling, Friedman [1953] and Simon [1963], clearly illustrate the two approaches [from Beinhocker 2005]: Friedman [1953]: The methodology of positive economics argued that unrealistic assumptions in economic theory do not matter as long as the theories make correct predictions. If the economy behaves as if people were perfectly rational, then it does not really matter whether they are or not. Assumptions do not require any more justification as long as the model works. Simon [1963]: Problems of methodology Discussion counter-argued that the purpose of scientific theories is not to make predictions, but to explain things. Predictions are then tests of whether the explanations are correct. The entire logical chain of explanation needs to be tested, not just the conclusion at the end. Given the ubiquitous nature of economics and its role in policy analysis, it is relevant to question how well the field performs in its model building activities. Casti [1990] compares economics with other model-based sciences on the basis of the models ability to predict and to explain. Prediction is defined as the ability of the model to foretell the behavior of a system given a set of model inputs. Explanation refers to the ability of the model to provide insight into system behavior in a simple and veridical manner. He claims that economic models perform poorly on both dimensions. As a reference point, Casti identified celestial mechanics as a discipline where models perform excellently on both prediction and explanation.

9 ECONOMIC SCIENCES: ON THE AUTOBAHN... 9 SIGNS OF TROUBLE? Despite the preeminence of the traditional neoclassical economics perspective, a number of commentators from within the field have begun to ask basic questions regarding the veracity of the models that are based on the traditional modes of economic thinking. For example [taken from Beinhocker 2005]: Joseph Stiglitz Anybody looking at these models would say that they can t provide a good description of the modern world. Gregory Mankiw given the low useful output of economists, less money should go into their research, and he [Mankiw] compared them to over-subsidized dairy farmers Alan Greenspan We really do not know how [the economy] works The old models just are not working. A surprising problem is that a number of economists are not able to distinguish between the economic models we construct and the real world. While these criticisms are directed at formal econometric models, they apply equally well to the informal mental models that economists (and all others) rely upon in their work and interactions with reality. Mental models are essentially maps of reality; difficulties arise when the map becomes confused with the actual terrain. A CRITICAL LOOK AT MODELS The theory of models [Chung and Keisler 1990] is that branch of mathematical logic that is concerned with the relationship between a formal language and its interpretations. Translating this into terms of the real world, we are interested in the relationship between natural processes (in reality] and the corresponding simplifications (the formal model) that are created to understand and to manage aspects of that reality. Since much of science is concerned with model building, we should be concerned with some of the basic issues and questions like: What is a model? What are the features of a good model? How can a natural process PN be represented in a formal system SF? What is the relationship between PN and SF? How can we compare two models SF1 and SF2 of the same process PN? When does similarity of two natural systems PN1 and PN2 imply that their models SF1 and SF2 are similar? These questions relate to the relationship of the economic model to reality. But to understand the development of the formal econometric model SF, we have to recognize that the economist s mental model exerts an unconscious, yet pervasive, influence on the questions that are asked and how they are answered. An important step in the direction of getting back on the Autobahn is to apply these critical questions to the mental models of the analyst. This is a process of critical inquiry that is experiencing a resurgence of interest in the social sciences.

10 10 C. BRONN MENTAL MODELS In thinking about the concept of models, we need to be aware that there are two types of models that are under consideration. One is the formal mathematical model that is the usual output of economic thinking and theorizing. This model is very public and (usually) well documented in terms of specifying obvious assumptions needed to assure consistency. The other model is called the mental model. This represents a personal map that guides individual behavior. The mental model is made up of deeply held assumptions and generalizations that influence and guide individual behavior [Senge 1990]. Mental models can be seen as the product of a reflective feedback process that tends to filter and select information that supports firmly held beliefs. The ladder of inference [Argyris 1990], shown as Figure 1. below, illustrates the reinforcing feedback loop that affects the development of a mental model. Take ACTION based on beliefs Adopt BELIEFS about the world Draw CONCLUSIONS Make ASSUMPTIONS based on the added meanings Add MEANING (cultural and personal) Select DATA from what is observed Observable data and EXPERIENCES (as a video recorder might captutre) Reflexiveloop our beliefs affect what data we select next time around Figure 1. The ladder of Inference Source: adapted from Ross [1994].

11 ECONOMIC SCIENCES: ON THE AUTOBAHN The relationship between explicit models and mental models is given in Figure 2. This diagram illustrates the single loop learning process. In this diagram, development of the formal model occurs in the Choosing phase of the loop. The inputs are empirical observations, which are subject to the biases and distortions identified in the ladder of inference, and by the effect of the mental model. In this learning process, the mental model is a type of exogenous factor; it is not affected by the on-going learning processes that utilize the formal model. However, the mental model establishes the context, or paradigm, within which the formal modeling activities are conducted. In this role, the mental model is crucially important in defining the formal model. Mental model (governing variables and relationships) Choosing Observing consequences (math/mismath with expectations) Single loop learning Acting Figure 2. The single loop learning process Source: adapted from Argyris and Schön [1978]. RECONSIDERING REALITY The main purpose of model building is to impose a set of simplifications to a complex reality that allows the essence to be understood but without the extraneous details that confuse the picture. By definition, all models are wrong since they are simplifications of a vastly complex reality. However, models can still be useful if they are able to capture relevant aspects of the situation in such a way as to facilitate prediction and/or explanation. The resulting formal model is the outcome of a series of simplifications and sense-making activities on the part of the analyst. Figure 3. illustrates the relationships among successive simplifications and created understandings. At the top level, Reality is the complex entity that is under study. From this reality, selected Events are generated through empirical observations and represent the observables that are of interest in an inquiry process regarding PN. Building on the set of events, the innate human tendency to make sense of our surroundings results in these events being organized into Patterns, which are the resulting behaviors or interactions of selected events among themselves or over time. Patterns are the basis for econometric modeling and can be employed for prediction. A significant toolbox of econometric methods has been developed to support this type of sense-making.

12 12 C. BRONN But at a deeper level there exist Structures that generate the observables in the Real World. These structures are expressions of the formal system, SF. In the language of systems thinking, each SF is merely a hypothesis about the underlying natural process PN in the reality under study. At the lowest level, this hierarchy of insight lies the Mental Model. The importance of these informal models is indicated by the dotted lines that point back to events, patterns, and structures. The interpretation is that the mental model influences the selection of data, the patterns of sense-making, and the development of formal models that are used for understanding and policy making. Mental models themselves are influenced by assumptions, beliefs, and the sum total of the individual s life experiences. Very often, we are not consciously aware of our mental models or the effects they have on our behavior [Senge 1990]. A central issue in this context is the degree of flexibility that an individual s mental model exhibits. The claim of this paper is that the challenge for continued development of economic science lies in how Reality is perceived. This has follow-on implications for both sensemaking and for model building. Despite the advances made by traditional economic theory and modeling, the earlier quotations from Stiglitz, Mankiw, and Greenspan suggest that the underlying mental models are approaching their limits of applicability in important cases. More specifically, the many assumptions that traditional economics theory is based upon are simply not as valid under current conditions as they may have been during less complex and dynamic times. Consequently, the correspondences between the formal models SF and the natural processes PN that are of current and future concern become weaker. Reality is the state of things as they actually exist, rather than as they may appear or may be thought to be. Compact OED of Current English (2005). Reality Events Other possible observations (events) Real World Patterns Structures Mental model influences selection and interpretation of... Abstract World Assumptions The set of concepts and theories articulates in standard educational literature and peerrevlewed journals. Figure 3. Level of insight Source: own elaboration. Mental Models Beliefs Those things we hold to be true, despite evidence to the contrary. J.O`Connor

13 ECONOMIC SCIENCES: ON THE AUTOBAHN Some of the features of reality that contribute to the challenge of building useful models include the following: Uncertainty. Complexity (both detail and dynamic). Adaptive behaviors. Dynamics. Open system, multiple nested sub-systems. Multiple agents, multiple objectives. Limited resource availability. Multiple processes, multiple equilibria. For example, in agriculture the relationship between agents in the agro-economic system is characterized by a complex set of relationships among many distinct actors. The actors can be identified as groups of stakeholders with generally similar interests within each stakeholder group, but with possibly dissimilar interests between stakeholders. The on-going relationships between stakeholder groups results in learning and adaptive behaviors among them. There will always be uncertainty surrounding many of the key factors in these relationships, with some elements being highly uncertain and possibly novel. Stakeholder groups will generally have their distinct goals that they strive to achieve. One group s actions to satisfy its objectives can adversely affect the ability of another group to achieve their objectives. Added to this difficulty is the fact that there are several different sub-systems interacting with each other as well. For example, the interactions of the legalregulatory system, the economic market system, the fundamental ecological system, and the socio-cultural system play an important role in the evolution of industrial agriculture. There are no system-wide optimum solutions in this dynamic and evolving complex environment. Any claim of such a solution for one particular group of agents will certainly result in reactions from others that can adversely affect the entire system. COMPLEX ADAPTIVE SYSTEMS The basic building block of a complex adaptive system is the agent [Dooley 1997]. Depending on the system, agents can take many different forms. In economic systems, examples include buyers, sellers, producers, regulators, and other intermediaries that comprise the network of interactions that characterize the system and drive its behavior. For example, a common stakeholder analysis is an exercise in identifying relevant agents in a particular context. Agents are semi-autonomous in that they have personal goals that they seek to achieve and they also, subject to constraints, continuously scan their environment in order to build an understanding of it. Decision rules may be modified in accordance with changes in the agents perception of their environment in order to improve the agents fitness measure. Agent interactions induce flows of information and resources that affect the state of the overall system. Agents are embedded in multiple feedback loops that continuously affect them, inducing new actions in response to changes in the local environment. Figure 4 presents a schematic view of a complex adaptive system.

14 14 C. BRONN Figure 4. A schematic complex adaptive system Source: adapted from Miller and Page [2007]. Complex adaptive systems are characterized by the following properties. Emergence: macro-level behavior from micro-level interactions. Co-evolution: learning and adapting to changing environmental conditions. Sub-optimalization: perfection is not necessary for survival. Requisite variety: more variety in a system increases resilience and strength. Connectivity: relationships between agents are usually more important than the individual agents themselves. Simple rules: emerging patterns of great variety can come from relatively simple governing rules. Iteration: small changes in initial conditions can have significant effects after having passed through emergence. Self-organization: there is no hierarchy of command and control, only a constant reorganizing to find the best fit to an environment. Edge of chaos: systems in equilibrium do not have the internal dynamics to enable it to respond rapidly to changes in its environment. Nestedness: systems within systems. An interesting feature of complex adaptive systems is the concept of upward and downward causality. The observable macro-level patterns, based on events, are the result of micro-level agent interactions. This is an example of upward causality and the agent level interactions represent the natural process PN. On the basis of these observable patterns, traditional economics has then developed its formal models SF. Using these models to develop policy, the direction of causality is now downward. The intention is that policy derived from the emergent behavior will influence the micro-level agent interactions in the direction that achieves the goals of the policy. For this to have a chance of succeeding, the correspondence between SF and PN must be significant. However, since policy is based on emergent behavior, the likelihood of close correspondence cannot be assured.

15 ECONOMIC SCIENCES: ON THE AUTOBAHN POLICY DESIGN ISSUES FOR CAS In social systems especially, change is often a specific intention of purposive human agents. Gaining insight into how the system may react to potentially significant changes should be an important factor in the design of change instruments and policies. Complex adaptive systems have unique characteristics that need to be taken into consideration, in addition to simply the objectives of the policy itself. These include the following [Ruhl 2008]. SENSITIVITY TO INITIAL CONDITIONS Due to feedback, non-linearity and emergence, relatively small changes in starting conditions can lead to relatively large differences in overall system dynamics. CONFLICTING CONSTRAINTS ON THE FITNESS LANDSCAPE Changes in one system component to promote fitness may be limited by properties of other system components also designed to promote fitness. CO-EVOLUTIONARY FITNESS LANDSCAPES Improvements in system A s fitness prompt adaptive co-evolutionary moves in other systems that could reduce A s fitness possibilities under its new configuration, prompting yet further adaptation in system A. IRREDUCIBILITY OF SYSTEM BEHAVIOR Because emergence is a system-wide phenomenon, system behavior cannot be understood and designed by studying a single agent or group of agents IRREVERSIBILITY OF SYSTEM STATES Because the present system is a product of all information that has flowed through the system to that point in all past states, the system dynamics cannot be reversed to past states, but only steered into new directions that approximate where the past might have led if different decisions had been taken. IMPERMANENTLY OPTIMIZABLE FITNESS Because of co-evolutionary fitness landscape effects, superior fitness cannot be locked in permanently and attempts to do so might be counterproductive. UNPREDICTABLE FUTURE STATES Taking all of the complex adaptive system properties into account, the future states and big events of a system are not predictable over relevant time horizons.

16 16 C. BRONN Meadows [1999] proposed a ranking of systems-based initiatives for effecting change in complex systems. The listing is based on the notion of leverage, which is similar to the idea of using mechanical leverage to amplify an input force. Systems have often been observed to respond to change in unexpected ways resistance to changes, unintended consequences, and counter-intuitive behaviors being the more common examples. It has also been observed that systems can be relatively insensitive to certain types of interventions (similar to price inelasticity), yet remarkably sensitive to others. These effects can be seen as responses to leveraged inputs to the system. The key management skill is to be able to find the high leverage points in the system. Computational approaches can support this effort [Sterman 2000; Epstein 2006]. The leverage effect increases down the list. However, it also becomes more difficult to implement policies as their effect increases. Not surprisingly, the most popular policy instruments, imposition of taxes and requiring standards such as the ISO standard for environmental management systems, also tend to be the least effective in this list. These instruments have a symbolic worth however policymakers, in the short term, appear to be making decisions and acting on them. Meadows system leverage points (in increasing effectiveness) 9. Numbers (subsidies, taxes, standards). 8. Material stocks and flows. 7. Regulating negative feedback loops. 6. Driving positive feedback loops. 5. Information flows. 4. The rules of the system (incentives, punishment, constraints). 3. The power of self-organization. 2. The goals of the system. 1. The mindset or paradigm out of which the goals, rules, feedback structure arise. CONCLUSION Ultimately, the vision for economic science must be to encourage a more critical perspective on itself and to work for improving the discipline s learning capabilities. Figure 2 presented a model of inquiry and learning that kept the underlying mental model outside of the learning process. Essentially, any changes to the mental model are incremental and likely not to be sufficient to keeping up with the dynamic reality that it attempts to understand and control. One solution is to recognize that the real world conditions have evolved to the point where economics require a paradigmatic shift. This demands that the existing mental models be re-evaluated and updated. The complex adaptive systems perspective and the associated methodologies require such a change. This is not to say that everything before must be abandoned, rather it reflects better the complexity of society. Figure 5 modifies the basic single loop learning model by adding a direct link from Observing consequences to the mental model. This is intended to emphasize the need to evaluate observations with expectations based on theory and to use mismatches as important signals regarding the veracity of the existing models. This learning process has two loops. The inner loop relates to the development of formal models, and the outer loop focuses on improving the quality of the informal mental models.

19 CHALLENGES ROCZNIKI FOR NAUK RESEARCH ROLNICZYCH, IN THE FIELD SERIA OF G, AGRICULTURAL... T. 98, z. 2, CHALLENGES FOR RESEARCH IN THE FIELD OF AGRICULTURAL ECONOMICS IN THE UK Kenneth J. Thomson 1 Professor Emeritus, University of Aberdeen, and Theme Leader James Hutton Institute, Aberdeen, United Kingdom S³owa kluczowe: ekonomika rolnictwa, Wielka Brytania Key words: agricultural economics, United Kingdom A b s t r a c t. Research challenges for agricultural economics in the United Kingdom (UK) can be classified under "issues", "methods" and "resources". Under "issues", those of environmental land management (e.g. for landscape, biodiversity, outdoor access) have received most attention, along with questions of food security, and of animal health and welfare. In contrast, farm management has been relatively neglected, in line with the traditional (and government-shared) inclination towards economic liberalism. In terms of "methods", new concepts, such as multifunctionality, payments for ecosystem services, and environmental valuation techniques, have predominated, with relatively little effort to model the EU Common Agricultural Policy. "Resource" issues have primarily arisen from the rather radical restructuring (including closure of many UK agricultural faculties) and stringent procedures (e.g. research assessment exercises) within the current and increasingly competitive UK university system. In this context, new staff, as well as ideas and funding, have often been sought, and gained, from elsewhere in the European Union. INTRODUCTION The challenges in the title of this paper can be characterised in several ways, including: issues: what scientific, economic or socio-political questions should they address? methods: how should agricultural economists analyse these issues? resources: how should the profession utilise its scarce time, expertise, data, etc.? Moreover, should challenges be interpreted as those tasks which the profession cannot easily undertake, or should it focus on those which are important but so far at least are being addressed successfully? How do challenges relate to priorities, as seen by agricultural economists themselves, or by stakeholders? 2 1 Thanks are due to Prof. Dr Arie Oskam of Wageningen Agricultural University and to Dr Holger Bergmann of Göttingen University for stimulating remarks on earlier drafts of this paper. However, neither colleague bears any responsibility for its contents. 2 Ahearn et al. [1998] describe a priority-setting process undertaken by the U.S. Council for Food, Agricultural and Resource Economics (C-FARE) for agricultural economics. Though specific to that time and country, the exercise is of wider interest.

20 20 K.J. THOMSON Taking advantage of these indeterminacies, this paper seeks to explore all of these avenues, though it cannot hope to do so thoroughly. It tries to cover the last decade or two 3, with an occasional focus on current issues, such as recent proposals for CAP reform. Some other terms on the subject matter should first be discussed. Research is taken here to mean original investigation undertaken in order to gain knowledge and understanding It includes work of direct relevance to the needs of commerce, industry, and to the public and voluntary sectors, scholarship, the invention and generation of ideas, images, etc. [HEFCE et al. 2008, Overview para. 19]. It excludes, for example, the development of teaching materials that do not embody original research, and the bulk of the work undertaken by economic and farming consultants (a large, influential and successful subsection of the profession in the UK), and by officials, agricultural statisticians, etc. In recent years indeed, decades the term Agricultural Economics has undergone intensive discussion in the UK 4. This debate has been stimulated by the unpopularity of modern commercial farming in Britain 5, and has occurred for a number of reasons, which include environmental degradation, food scares, and budget cost. The number of British university research centres with more than one or two agricultural economists on their staff has fallen from perhaps twenty in the 1980s to around five (Reading west of London, Newcastle in northern England, Harper Adams near the Welsh Borders, the Scottish Agricultural College mainly in Edinburgh, and Queens University in Belfast). In the light of falling student demand, other well-known centres, including the Universities of London (Wye College), Manchester, Exeter, Aberystwyth, Edinburgh and Aberdeen, have all closed their Agriculture faculties or Agricultural Economics departments, and/or they have moved remaining agricultural economics staff into schools of business, environmental sciences, etc. Individual or small groups of agricultural economists remain at other centres, such as the Universities of East Anglia, Exeter, Gloucestershire, London (Imperial College), Kent, Nottingham, and Sussex, the Royal Agricultural College at Cirencester, and the James Hutton (previously Macaulay) Institute in Aberdeen. Many of these staff are in larger and often multi-disciplinary business, rural or environmental units. This is not to say that Agricultural Economics is disappearing as a discipline and profession in the UK; it still runs a successful Society 6 and journal (the Journal of Agricultural Economics); it is largely responsible for the EuroChoices periodical; it supplies many leading international agricultural economists, e.g. at OECD and FAO; and many students and researchers still come to the UK from around the world for training and collaborative work. It can probably claim to have initiated and stimulated new thinking in many policy and academic circles outside Britain, ranging from the European Commission and Parliament to the European and International Associations of Agricultural Economists. As an indication of UK professional activity (perhaps biased towards those of its more senior members), Table 1 lists the Presidential Addresses delivered to the Society over the last 20 years. 3 A history of agricultural economics in Britain over most of the last century is available from Colman and Lowe [1990]. See also Giles [1987]. 4 As also in the United States: see the recent change of name from the American Agricultural Economics Association to the Agricultural and Applied (not now American!) Economics Association: see 5 Agriculture accounts for 0.6% of UK Gross Value Added (similar to GDP), and for 1.6% of the national workforce. These figures indicate low incomes from farming even after CAP receipts (not included in GVA) are considered. The agri-food sector as a whole accounted for 6.7% of GVA and 14% of the workforce. Farming occupies about 75% of the total UK land area, roughly 25% each in crops, grass and rough grazing. 6 The UK Agricultural Economics Society has nowadays about 350 members, a fall from about 400 around 1990.

21 CHALLENGES FOR RESEARCH IN THE FIELD OF AGRICULTURAL Table 1. UK Agricultural Economics Society Presidential Addresses 2011: Semi-Subsistence Farming (Davidova) 2010: EU/US Agricultural Policy (Blandford*) 2009: Bioenergy (Swinbank) 2008: Farm Incomes 2007: Rural Environment and and Wealth (Hill) Governance (Hodge) 2006: Environmental Accounting (Buckwell+) 2005: International Trade and Marketing (Josling*) 2004: Economic Theory and the Social World 2003: Policy Evaluation (Legg*+) 2002: Small-scale Farming in Africa 2001: Rural Development (Thomson) 1999: Environment (Whitby) 1998: Farm Management (Webster) 1997: Food Marketing (Ritson) 1996: Veterinary Economics 1995: Marketing (Bansback+) (McInerney) 1994: Agri-Environmental Schemes 1993: Farm Management (Dancey+) 1992: Self-Sufficiency Policy (Sturgess) 1991: Macroeconomics (Peters) (Belshaw) (Colman) (Harvey) * Author working outside the UK + Author working outside academia at time of delivery Source: The above papers can be downloaded (given appropriate permissions) via [http://onlinelibrary.wiley.com/journal/ /(issn) ]. ISSUE CHALLENGES No-one can complain that, globally, Agricultural Economics lacks challenges; after a decade or two of relative quiescence while the Green Revolution and its aftermath saved the world from the food shortages and famines predicted by the Club of Rome s Limits of Growth [Meadows et al. 1972], recent rises in farm commodity prices have triggered renewed concern with food security around the world. Agricultural Economics is widely understood to have contributions to make towards resolving the paradox of adequate food availability existing alongside widespread food poverty and occasional famines, whether at the conceptual level ( What is food poverty? ), at the farm level, especially in developing countries, and at the policy level, e.g. the WTO and the Doha Round. It can also contribute to analysis of the underlying drivers of population growth, climate change, consumer demand and technological development. Many of these problems have been analysed in a recent report from the UK government (BIS, 2011) though mainstream UK agricultural economists were not prominent in this project s lead panel.

22 22 K.J. THOMSON The standard UK perspective on these issues is shared (perhaps formed) by most UK economists open trade in food commodities (and in farmland), encouragement of technological innovation along the food chain, and reliable information for consumers and governments. In addressing these challenges, British agricultural economists can play many roles, but most involve international collaboration and/or multi-disciplinarity and perhaps patience and optimism, given the persistence and intractability of many of them. Within Europe, there is again no shortage of stated challenges from various authorities, including the European Commission itself. These extend well beyond the Common Agricultural Policy (CAP), to the EU budget, the accession of more Member States, the problems of the euro-zone (to which the UK does not belong), and the Europe 2020 vision of a smart, sustainable and inclusive economy [http://ec.europa.eu/europe2020]. However, agricultural economists are naturally focussed, as always, on CAP reform, in particular recent proposals from the Commission [2010] and the European Parliament [2010, 2011]. These identify the main challenges facing the CAP as food security, environment and climate change and territorial balance, and cover the future of both direct payments to farmers and of the rural development Pillar 2. The position of the UK, and hence of many of its agricultural economists, in this debate is complex. The UK government in London considers that the Commission s (and probably the Parliament s) proposals are lacking in ambition, and it wishes a fundamentally different CAP, with Pillar 2 taking a larger share of limited resources [Spelman 2011]. This of course brings in the higher-level issues of the size of the overall EU budget (which the UK wishes to be significantly less), and the UK rebate agreed in However, the three UK devolved administrations for Scotland, Wales and Northern Ireland, which contain 47% of farmland in Britain, and attract a significant share of CAP funding are far less radical. They have developed a joint position on CAP reform, stressing a fair and proportionate share of the budget, flexibility to respond to specific local needs, and simplification [Scottish Government 2011]. Moreover, recent elections have strengthened the voice of these administrations, for example with an independent Scotland now a real possibility in 5 or 10 years time. A further factor is the unusual structure of British farming and food, with a high proportion of large commercial businesses in food production, processing, manufacture and retailing, and the correspondingly low prominence of small-scale enterprises in these sectors although general UK affluence accounts for a good deal of part-time hobby farming (as well as large landed estates ), specialist foodstuffs (biscuits, whisky, cheeses, even wine) with domestic and export prospects, and niche retailing (farmers markets, online purchasing). Despite these disparate domestic political voices in the EU CAP debate, most UK agricultural economists are probably aligned more with London than with Edinburgh, Cardiff and Belfast. They are therefore faced with analysing a Policy with which they, like their main government, are out of sympathy, and for which little research funding has been forthcoming in recent decades. Moreover, the current CAP in one form or another enjoys strong support in many other EU Member States, so that radical reform of the type advocated in London and by many economists is highly unlikely. The best that can be done in such circumstances seems to be a combination of emphasis on fundamental economics (efficiency, comparative advantage, free markets, etc.) and detailed analysis of Axis 2, on which most UK Pillar 2 funding is focussed. The latter challenges involve the valuation of environmental goods and assets, and questions of institutional economics such as governance. By contrast, issues of fairness (of Pillar 1 direct payments) and agricultural modernisation (Axis 1) receive comparatively little attention from UK researchers.

23 CHALLENGES FOR RESEARCH IN THE FIELD OF AGRICULTURAL Within the UK, agricultural economists have had perhaps paradoxically rather little to say about British agriculture itself. There are political and industry concerns over various matters, such as returns in the farm-to-food chain, and the structure of the farming (and food retailing) sector, especially the alleged lack of young farmers or new entrants, and the domination of the supermarkets. However, most agricultural economists consider these questions as of little inherent significance, being merely symptoms of an industry under (necessary) adjustment, and of desirable growth in economic efficiency. Farm income levels receive little attention partly due to their dependence on subsidy at a time when the political atmosphere is generally sceptical of government (e.g. David Cameron s Big Society ), and partly due to economic evidence [Hill 1996] that British farm family incomes from all sources are comparable with others, and that their wealth in terms of land is considerably higher than most of the population. Instead, domestic issues have, as mentioned above, focussed on ways of protecting the famous British countryside and landscape against the encroachments of monoculture, farm-sourced pollution, and privatisation (new legislation on public access to land). The variety of UK topography, and its rich cultural history, mean that such work is often carried out via case studies or for specific regional challenges, e.g. for eastern wetlands, Scottish crofting (small farm holdings with special land tenure), extensive upland farming of sheep and cattle, peri-urban green belts, etc. Other areas of particular British emphasis have been the economics of animal health and welfare (including wild animals such as foxes and badgers as well as farm livestock), agriculture in developing countries (particular propoor policies), and some risk aversion (though not market risk management). Some British agricultural economists are working in the area of climate change and carbon budgets related to farming as a result of strong national commitments to greenhouse gas emissions and renewal energy generation. So far, such work has had little impact on field-level activity, perhaps because high UK fuel prices attract greater attention, but there is debate over the efficiency and visual impact of wind farms (energy cropping is not yet significant). METHOD CHALLENGES Compared to the issue challenges described above, the particular challenges facing British agricultural economists as regards the methods used in their work may be expected to be more similar to those encountered elsewhere 7. However, the extent to which these method challenges are being successfully addressed depends to some extent on the structure and resources of the profession: see next section. Given the stressing of new methods in the social sciences and their (usually English language) journals generally, and the influence of the publications-based UK Research Assessment Exercise 8 on university funding and staff promotion, it might be expected that challenges of this type would be identified and tackled quite energetically within the UK. In some ways, this expectation seems generally true, at least as regards the issue challenges highlighted above. UK agricultural (and rural, environmental) economists have been 7 The question why agricultural economists stay with or in rarer cases turn to agriculture as the focus of their disciplinary work is not addressed here. Nor are the employment hopes and out-turns of agricultural economics graduates explored. 8 Now to be the Research Excellence Framework (REF) 2014; see

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